Stop shipping AI Slop
An agent can finish every run without an error and still give worse answers, cost more, or lose users. Anosys puts agent traces, evals, token spend and product analytics in one place, so you can see what changed, why, and what it cost, then check that the fix worked.
A short walkthrough of the console on a live agent application.
The run completes, the logs look clean, and something is still wrong. These are the problems Anosys is built to catch.
No errors, worse answers
A prompt or model change can lower answer quality or task success without failing a single request. Judges on sampled production traces show the drop day by day.
A retry loop nobody noticed
A tool that keeps failing can resend the whole context on every turn. Per-run tokens and cost show which agent and which tool it was.
Every service is green, users still leave
Join agent traces with sessions, funnels and page events to see where people give up after an AI step.
Did the last change help?
Compare eval scores, cost, latency and user behavior before and after a change, instead of judging it from a few demos.
Anosys collects the signals, joins them per run, user and session, shows what changed, and gives you the tools to act on it.
AI operational intelligence
Ingest
SDKs for OpenAI Agents, Anthropic, Claude Code and Codex, plus OpenTelemetry, REST, the web tracker and file drops.
Correlate
Join runs, model calls, tools, users, sessions, costs and business events on shared IDs.
Explain
Find what changed and where: regressions, slow or expensive steps, and drops in user or business metrics.
Debug agent runs
Open the trace of a bad run and see which step, tool or handoff caused it.
Cut AI spend
Cost by agent, tool, model and user, coding assistants included, next to quality.
Evaluate quality
LLM judges, datasets, experiments and human review on real production traces.
Alert and govern
Alerts and AutoJudge, field encryption, retention and access controls.
Then measure the next change against the same evidence.
The screens you work in day to day. Names in these screenshots are placeholders.
Follow an agent run step by step
Agents, turns, model calls, tool functions and handoffs on one timeline, with the duration, inputs and outputs of every span. Go straight to the slow, failing or expensive step.
Agent tracing →
Score production traces with LLM judges
Start from templates such as relevance or groundedness, version the prompt, criteria and thresholds, and run them on a schedule. Calibrate judges against human review.
Evaluations →
Dashboards that start from templates
Templates for website and product analytics, agent traces, and Claude Code or Codex spend. Build your own from 20+ widget types, from funnels and Sankey diagrams to trace trees.
Dashboards →
Ask the AI Assistant
Ask about your own data in plain English. The assistant queries it, explains the answer, and can create the widget, pipeline or alert for you. Suggested questions are tied to the datasets you have.
AI Assistant →
Use an SDK, an open protocol, a script tag or a file. There is no proprietary agent to install.
Send JSON events and metrics to an HTTP endpoint.
OTLP traces, metrics and logs from any OTel SDK or collector.
One script tag for page views, engagement, scroll depth and Web Vitals, or an image pixel.
Google Cloud Storage and FTP file drops. S3 on request.
Each integration has a setup guide in the docs.
Also Antigravity, and anything that speaks OpenTelemetry or HTTP. All integrations →
Send your first trace
Start free with an SDK or the web tracker, or book a demo and we'll walk through it on your own data.
